1. Field of the Invention
Embodiments of the invention provide techniques for computationally analyzing a sequence of video frames. More specifically, embodiments of the invention relate to techniques for an adaptive update of background pixel thresholds in response to detecting a sudden illumination change.
2. Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, some currently available systems are configured to identify and track objects moving within a sequence of video frame using a frame-by-frame analysis. These systems may be configured to isolate foreground elements of a scene from background elements of the scene (i.e., for identifying portions of a scene that depict activity (e.g., people, vehicles, etc.) and portions that depict fixed elements of the scene (e.g., a road surface or a subway platform). Thus, the scene background essentially provides a stage upon which activity occurs. Some video surveillance systems determine the difference between scene background by generating a model background image believed to provide the appropriate pixel color, grayscale, and/or intensity values for each pixel in an image of the scene. Further, in such systems, if a pixel value in a given frame differs significantly from the background model, then that pixel may be classified as depicting scene foreground. Contiguous regions of the scene (i.e., groups of adjacent pixels) that contain a portion of scene foreground (referred to as a foreground “blob”) are identified, and a given “blob” may be matched from frame-to-frame as depicting the same object. That is, a “blob” may be tracked as it moves from frame-to-frame within the scene. Thus, once identified, a “blob” may be tracked from frame-to-frame in order to follow the movement of the “blob” over time, e.g., a person walking across the field of vision of a video surveillance camera.
Further, such systems may be able to determine when an object has engaged in certain predefined behaviors. However, such surveillance systems typically require that the objects and/or behaviors which may be recognized by the system to be defined in advance. Thus, in practice, these systems simply compare recorded video to predefined definitions for objects and/or behaviors. In other words, unless the underlying system includes a description of a particular object or behavior, the system may not recognize that behavior (or at least instances of the pattern describing the particular object or behavior). Thus, to recognize additional objects or behaviors, separate software products may need to be developed. This results in surveillance systems with recognition capabilities that are labor intensive and prohibitively costly to maintain or adapt for different specialized applications. Further still, such systems are often unable to associate related aspects from different patterns of observed behavior. As a result, by restricting the range of objects that a system may recognize using a predefined set of patterns, many available video surveillance systems have been of limited usefulness.